Generative adversarial network medical image generation for training of a classifier

ABSTRACT

Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for providing agenerative adversarial network medical image generation for training aclassifier.

Generative models learn a joint probability distribution p(x, y) ofinput variables x (the observed data values) and output variables y(determined values). Most unsupervised generative models, such asBoltzmann Machines, Deep Belief Networks, and the like, require complexsamplers to train the generative model. However, the recently proposedtechnique of Generative Adversarial Networks (GANs) repurposes themin/max paradigm from game theory to generate images in an unsupervisedmanner. The GAN framework comprises a generator and a discriminator,where the generator acts as an adversary and tries to fool thediscriminator by producing synthetic images based on a noise input, andthe discriminator tries to differentiate synthetic images from trueimages.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising a processor and a memory, the memorycomprising instructions that are executed by the processor to configurethe processor to implement a machine learning training model. The methodcomprises training, by the machine learning training model, an imagegenerator of a generative adversarial network (GAN) to generate medicalimages approximating actual medical images. The method also comprisesaugmenting, by the machine learning training model, a set of trainingmedical images to include one or more generated medical images generatedby the image generator of the GAN. Moreover, the method comprisestraining, by the machine learning training model, a machine learningmodel based on the augmented set of training medical images to identifyanomalies in medical images. In addition, the method comprises applyingthe trained machine learning model to new medical image inputs toclassify the medical images as having an anomaly or not.

In some illustrative embodiments, a method is provided, in a dataprocessing system comprising a processor and a memory, the memorycomprising instructions that are executed by the processor to configurethe processor to implement a generative adversarial network (GAN). Themethod comprises configuring a discriminator of the GAN to discriminateinput medical images into a plurality of classes comprising a firstclass indicating a medical image representing a normal medicalcondition, one or more second classes indicating one or more abnormalmedical conditions, and a third class indicating a generated medicalimage. The method further comprises generating, by a generator of theGAN, one or more generated medical images and inputting, to thediscriminator of the GAN, a training medical image set comprising afirst subset of labeled medical images, a second subset of unlabeledmedical images, and a third subset comprising the one or more generatedmedical images. Moreover, the method comprises training thediscriminator to classify training medical images in the trainingmedical image set into corresponding ones of the first class, the one ormore second classes, and the third class. Furthermore, the methodcomprises applying the trained discriminator to a new medical image toclassify the new medical image into a corresponding one of the firstclass or one or more second classes. The new medical image is eitherlabeled or unlabeled.

In still other illustrative embodiments, a method is provided, in a dataprocessing system comprising a processor and a memory, the memorycomprising instructions that are executed by the processor to configurethe processor to implement a generative adversarial network (GAN). Themethod comprises training the GAN based on labeled image data, unlabeledimage data, and generated image data generated by a generator of theGAN. The GAN comprises a loss function that comprises error componentsfor each of the labeled image data, unlabeled image data, and generatedimage data which is used to train the GAN. The method further comprisesidentifying the new data source for which the trained GAN is to beadapted, and adapting the trained GAN for the new data source. Moreover,the method comprises classifying image data in the new data source byapplying the adapted GAN to the data in the new data source. Adaptingthe trained GAN comprises obtaining a minimized set of labeled imagesand utilizing the minimized set of images to perform the adapting of thetrained GAN.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example block diagram of a generative adversarial network(GAN);

FIGS. 2A and 2B are example diagrams illustrating a semi-supervisedgenerative adversarial network (GAN)-based architecture in accordancewith one illustrative embodiment;

FIG. 3 is an example diagram illustrating example sample chest X-rayimages utilized to demonstrate the advantages of the illustrativeembodiments of the present invention;

FIG. 4 is an example diagram illustrating example sample generated(fake) chest X-ray images generated by a generator of thesemi-supervised GAN-based architecture of the illustrative embodiments;

FIG. 5A is an example plot of accuracy performance of thesemi-supervised GAN architecture of the illustrative embodimentscompared to a conventional supervised convolutional neural network(CNN);

FIG. 5B is a table demonstrating a reduced numbered of labeled medicalimages required to achieve a comparable accuracy between thesemi-supervised GAN of the illustrative embodiments and a conventionalsupervised CNN;

FIG. 6 is a table demonstrating robustness of the semi-supervised GAN ofthe illustrative embodiments to overfitting to domain source artifactswhen compared to a conventional supervised CNN;

FIG. 7 is an example block diagram illustrative an implementation of thesemi-supervised GAN of the illustrative embodiments of with a cognitivesystem providing a medical image viewer in accordance with oneillustrative embodiment;

FIG. 8 is an example block diagram of a data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 9 is a flowchart outlining an example operation for training asemi-supervised GAN in accordance with one illustrative embodiment; and

FIG. 10 is a flowchart outlining an operation for re-training asemi-supervised GAN for a new data source in accordance with oneillustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for providing agenerative adversarial network (GAN) based framework for generatingmedical image data and training a medical image classifier based on anexpanded medical image dataset. The illustrative embodiments furtherprovide mechanisms for selecting a training methodology, using the GANbased framework of the illustrative embodiments, to be used for newsources of medical image data, such as when a new client of a medicalimage classifier service begins to utilize the classifier trained by theGAN based framework. In one illustrative embodiment, the methodologiesfrom which a training methodology is selected may comprise a firstmethodology in which the classifier is trained based only on a knownlabeled medical image source, i.e. based on labeled medical image dataonly, and a second methodology in which the classifier is trained basedon both known labeled medical image source data and the new source'smedical image data, acting as unlabeled medical image data in accordancewith the illustrative embodiments.

Deep learning algorithms require large amounts of labeled (annotated)data to train effective models for the performance of cognitiveoperations, such as image classification or the like. In medicalimaging, data is not as abundant as other computer vision fields due toprivacy laws, health industry standards, the lack of integration ofmedical information systems, and other considerations. While efforts toalleviate these issues are ongoing, currently these efforts hamper thespeed of innovation of deep learning algorithms as they inherentlyrequire large amounts of data for tasks such as image classification orsemantic segmentation.

In many cases, even if medical image data is available, the data isunstructured and lacks proper labeling or annotations, e.g., labelinganatomical structures within the medical image, measurements,abnormalities, or the like. To address this, one needs to annotate themedical images. However, annotation of medical images is an expensive,time-consuming, and largely manual process. Often, one can only feasiblylabel (annotated) a small portion of the available unstructured medicalimage data while having a much larger portion of unlabeled images. As aresult, medical imaging and computer vision cognitive operations arelimited to being able to use only small amounts of labeled images, e.g.,medical images, for use in training the classifier models, such asconvolutional neural networks, of cognitive logic for performingcognitive classification operations, e.g., medical image classificationtasks that identify diseases or abnormalities in the medical images.

Moreover, even if a particular labeled (annotated) image dataset isaccessible for training the classifier model of the cognitive systemlogic, the classifier still struggles to maintain the same level ofperformance, e.g., accuracy, on a different medical imaging dataset froma new or never-seen data source domain. In other words, many deeplearning classifiers tend to overfit to a particular data domain source.That is, for any given image classification task in medical imaging, onestrives to train a classifier that separates images based on thestructural or physiological variations that define the target classes.However, there are other sources of variance, such as scanner type andimaging protocol, that can differentiate images from one another. As aresult, when deep learning classifiers are trained on a particulartraining dataset, and then tested in production on data from a differentdomain source, there is usually a reduction in performance.

Previously, the approach used to solve the problem of labeled(annotated) dataset scarcity, and specifically the lack of labeledmedical image dataset samples in disease categories at the time oftraining the classifier model, was to use the available labeled datasetsamples from a normal class to train a segmentation model, e.g., aneural network or other model for segmenting the medical image intoseparate segmented parts (sets of pixels) that are computationallyeasier to analyze and/or are potentially more meaningful to theclassification operation. The features produced by this segmentationmodel are used along with the whole image to train the classifier model.This is a way of learning the distribution of data in one class, andtaking advantage of the learning in distinguishing that class from otherclasses. This concept of “learning normal” as a way to improveabnormality classification has also been used in generative machinelearning models.

Generative machine learning models have the potential to generate newdataset samples. The two main approaches of deep generative modelsinvolve either learning the underlying data distribution or learning afunction to transform a sample from an existing distribution to the datadistribution of interest. In deep learning, the approach of learning theunderlying distribution has had considerable success with the advent ofvariational auto-encoders (VAEs), such as described in Kingma, et al.,“Auto-Encoding Variational Bayes,” arXiv preprint arXiv:1312.6114(2013). VAEs attempt to find the variational lower bound of theprobability density function with a loss function that consists of areconstruction error and regularizer. Unfortunately, in thisformulation, the bias introduced causes generated images to appearqualitatively smooth or blurry.

Generative adversarial networks (GANs) were introduced in Goodfellow, etal., “Generative Adversarial Nets,” Advances in Neural InformationProcessing Systems, 2014. GANs utilize two neural networks referred toas a discriminator and a generator, respectively, which operate in aminimax game to find the Nash equilibrium of these two neural networks.In short, the generator seeks to create as many realistic images aspossible and the discriminator seeks to distinguish between images thatare real and generated (fake) images.

FIG. 1 is an example block diagram of a generative adversarial network(GAN). As shown in FIG. 1 , the generator, G, takes a vector z, sampledfrom random Gaussian noise or conditioned with structured input, andtransforms the noise to p_(G)=G(z) to mimic the data distribution,N_(data). Batches of the generated (fake) images and real images aresent to the discriminator, D, where the discriminator assigns a label 0for real or a label 1 for fake. The cost of the discriminator, J^((D)),and generator, J^((G)), are as follows:J ^((D))=−½*(E _(x-data)[log D(x)])−½*(E _(z)[log(1−D(G(z))])  (1)J ^((G))=−½*E _(z)[logD(G(z)]  (2)

With an appropriate optimization technique, the neural networks of thegenerator G and discriminator D may be trained to reach an optimalpoint. The optimal generator G will produce realistic images and theoptimal discriminator D will estimate the likelihood of a given imagebeing real.

The illustrative embodiments set forth herein train a GAN for generatingand discriminating medical images and utilize the generator G to createrealistic medical images as a data augmentation technique for trainingan abnormality detector (also referred to as a classifier) that operateson medical images. For example, the medical image generation isperformed by a trained generator G of the GAN while the abnormalitydetector, or classifier, may be implemented as the trained discriminatorD of the GAN, although in some illustrative embodiments, the classifiermay be a different neural network, cognitive classifier logic, or thelike, which is trained based on the expanded training medical imagedataset comprising a small set of labeled medical images, and a largerset of real and/or generated (fake) unlabeled medical images. In oneillustrative embodiment, the medical images may be chest X-ray images,however, the present invention is not limited to such. Rather themechanisms of the illustrative embodiments may be applied to any medicalimages of various anatomical portions of a biological entity, e.g., ahuman being, animal, or plant, using any of a variety of differentmedical imaging modalities, e.g., X-ray, computed tomography (CT) scan,sonogram, magnetic resonance imaging (MRI), or the like.

As noted above, medical imaging datasets are limited in size due toprivacy issues and the high cost of obtaining annotations. Augmentationof a dataset is a widely used practice in deep learning to enrich thedata in data-limited scenarios and to avoid overfitting. However,standard augmentation methods that produce new examples of data merelyinvolve varying lighting, field of view, and spatial rigidtransformations, for example. These modifications, while generatingslightly different images, do not capture the biological variance ofmedical imaging data and could result in unrealistic images. In otherwords, the modifications made to generate new medical images fortraining do not improve the training because the differences are notconsequential to the actual differentiation between normal and abnormalmedical images evaluated by the classifier.

The illustrative embodiments recognize that generative adversarialnetworks (GANs) provide an avenue to understand the underlying structureof image data which can then be utilized to generate new realisticmedical image samples. The illustrative embodiments utilize a GAN basedmechanism for producing an augmented set of medical images, e.g., chestX-ray images in one illustrative embodiment, to increase a size of thetraining medical image dataset. That is, in some illustrativeembodiments an architecture is provided that converts the GAN into asemi-supervised classifier for abnormality detection in medical images,trained on a fairly small size initial annotated medical image dataset.The augmented, or expanded, training medical image dataset generated byoperation of the GAN of the illustrative embodiments may be used totrain a convolutional neural network, or other machine learning model,or cognitive classification logic (collectively referred to as aclassifier herein), to classify images with regard to abnormalitypresence, e.g., cardiovascular abnormalities.

In some illustrative embodiments, this classifier may be implemented asthe discriminator D of the GAN, which may be implemented as aconvolutional neural network. The discriminator D is trained based onlabeled real medical images, unlabeled real medical images, andunlabeled generated (fake) images so as to be able to discriminatebetween these types of images. Thereafter, the discriminator D may beutilized with actual input medical image data to differentiate betweennormal and abnormal medical images, i.e. images where a disease ispresent and images where a disease is not present.

The augmentation mechanisms and resulting training of the convolutionalneural network, machine learning model, or cognitive classificationlogic of the illustrative embodiments provide higher accuracy forclassifying normal versus abnormal medical images (those having anabnormality present) when compared to known techniques. Moreover, theillustrative embodiments provide automated mechanisms for expanding oraugmenting the initial small size dataset of annotated medical images,which significantly reduces the amount of time, resources, and humaneffort needed to produce a sufficient size training medical imagedataset for training a classifier. Furthermore, the resulting trainedclassifier is more tolerant of data presented for classification fromnew domain sources as the trained classifier is trained to operate onunlabeled real image data.

The illustrative embodiments provide a framework by which to build adiscriminator that separates images depicting abnormalities, e.g.,disease instances, from normal medical images, e.g., normal chest X-rayimages. In order to build such a discriminator, the framework of theillustrative embodiments utilize a GAN in semi-supervised training. Thesemi-supervised GAN-based framework, or architecture, involves anadaptation of a GAN generator G to take advantage of both labeled andunlabeled data. As the GAN-based framework converges, the discriminatorD separates generated medical images from real medical images, as wellas real medical images representing abnormalities from real medicalimages representing normal medical images, i.e. those that do not havean abnormality (e.g., disease) present in the medical image. As aresult, both labeled and unlabeled data can contribute to theconvergence of the model. This is useful for scenarios where there is asmall amount of labeled (annotated) medical image data, or no labeledmedical image data, and a large amount of unlabeled (non-annotated)medical image data.

The training of the generator of the GAN to generate fake or syntheticmedical images that approximate real medical images to a level that thediscriminator of the GAN is fooled by the generated medical images,permits the generator to be used as an additional source of unlabeledmedical images that may be used to expand a training medical image dataset that can be used to train a discriminator that is configured, by themechanisms of the illustrative embodiments, to evaluate labeled,unlabeled, and generated (fake) medical image data. The training of thediscriminator is performed such that the discriminator is able todetermine the features of medical images indicative of real normal, realabnormal, fake normal, and fake abnormal medical images and provide highaccuracy in the classification of input medical images into theseclasses. The training of the discriminator to operate on unlabeledmedical image data makes the discriminator robust to re-training oradaptation based on new sources of medical imaging data.

Thus, when the GAN is employed with a new data source, such as a newclient of the GAN based classifier service, the GAN may be adapted orre-trained for use with the new data source in a manner that does notrequire time consuming and resource intensive processes for labelingmedical imaging data. That is, because the GAN is trained to obtain highaccuracy with a relatively small set of labeled medical imaging data, inone methodology the adaptation or re-training of the GAN for the newdata source may involve training the GAN on a small set of known labeledmedical image data itself, such as from a known trusted source, e.g., aknown trusted third party source of medical image data such as NationalInstitute of Health (NIH) medical image data sources, for example. Insuch an embodiment, the medical image data obtained from known trustedsource may be medical image data comprising medical images from asimilar medical domain as the medical images that are provided in thenew data source, e.g., if the new data source provides medical imagesfor cardiovascular disease evaluations, then the medical images obtainedfrom the known trusted source may similarly be labeled medical images inthe cardiovascular disease domain.

Alternatively, the methodology for adapting or re-training the GAN maycomprise training the GAN using a small set of known labeled medicalimage data as well as a relatively larger set of unlabeled medical imagedata from the new data source. This may require some efforts on the partof subject matter experts (SMEs) to label a small subset of the new datasource's data to allow for re-training or adapting of the GAN, but thissmall set of medical image data that is labeled is significantly smallerthan the full set of medical image data in the new data source and thus,is a minimized set of medical image data requiring a significantlyreduced amount of effort and resources than would otherwise be requiredshould the entire medical image data of the new data source be requiredto be labeled. That is, as the GAN is configured to perform training onboth labeled and unlabeled medical image data, the new data source'sunlabeled medical image data may be used in the re-training withoutneeding the owners or operators of the new data source to label theirmedical image data.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, the illustrative embodiments of the present inventionprovides a methodology, apparatus, system and computer program productfor performing medical image dataset augmentation, or expansion, using agenerative adversarial network framework or architecture. Theillustrative embodiments adapt a GAN mechanism to operate on reallabeled medical image data, real unlabeled medical image data, andgenerated medical image data generated by a generator G of the GANmechanism. The generator G receives random noise and generates a fakeimage. The discriminator D is adapted to receive real labeled imagedata, real unlabeled image data, and generated image data and operateson the data to identify whether the input medical image data is amedical image representing a real normal image, a real abnormal image,or a fake (generated) image. The GAN is trained using the real labeledimage data, real unlabeled image data, and generated image data suchthat as the model converges, the discriminator D separates generatedfrom real medical images, and further separates real disease medicalimages, or abnormal medical images in which an abnormality is present,from real normal medical images. As a result, both labeled and unlabeleddata can contribute to the convergence of the model.

Once the GAN is trained in this manner, the generator G may be appliedto generate fake (generated) images to augment, or even replace, atraining set of images, for training of a classifier, such as aconvolutional neural network (CNN) or other machine learning model. Forexample, a CNN disease classifier, such as the discriminator D of theGAN or another external CNN disease classifier, may be trained usinggenerated (fake) medical images and actual images. As the fake imagesgreatly approximate true images, they provide an augmentation to theactual (real) medical images in the training image dataset, with theexpanded training image dataset providing greater accuracy in thetraining of the CNN disease classifier. Once the CNN disease classifieris trained using the augmented set of training medical images, the CNNdisease classifier may be used with new medical images to classify themedical images as indicating disease or abnormality presence ornormality.

FIGS. 2A and 2B are example diagrams illustrating a semi-supervisedGAN-based framework or architecture in accordance with one illustrativeembodiment. FIG. 2A is an example block diagram illustrating thesemi-supervised GAN-based framework, while FIG. 2B is a more graphicalrepresentation of the semi-supervised GAN-based framework, in accordancewith one illustrative embodiment. As shown in FIGS. 2A and 2B, the GANcomprises a generator G 210 that receives a noise vector z, which may besampled random Gaussian noise or condition with structured input. Thegenerator G 210 is a convolutional neural network that transforms thenoise vector z 205 input into an unlabeled image. The discriminator D250 is a convolutional neural network that attempts to discriminate theinput image data into one of a plurality of classes, e.g., real imagenormal, real image abnormal, or generated (fake) image. In one exampleembodiment, the discriminator D 250 outputs a vector 260 having valuesindicating whether the discriminator D 250 has determined the inputimage data to be associated with a real image normal, real imageabnormal, or fake image.

In some implementations, such as that shown in FIGS. 2A and 2B, thediscriminator D 250 comprises K+1 output nodes for outputting values ofthe vector 260 indicative of which of K+1 classes the discriminator D250 has determined the input medical image data to be associated with.That is, there may be K classes in which one of the K classes is anormal medical image, i.e. a medical image in which no abnormality isidentified. Other classes of the K classes may represent different typesof abnormalities for which the discriminator D 250 is trained toidentify. The K+1 class is a class for generated or fake medical imagedata. For example, the discriminator D 250 may classify medical imagesas either real-normal, real-abnormal (e.g., cardiovascular diseasepresent), or fake and the corresponding labels in the output vector 260may be set to values indicative of the classification, e.g., 0 or 1associated with corresponding vector slots associated with the differentclasses.

In other illustrative embodiments, the discriminator D 250 may have 2Koutput nodes corresponding to 2K classes and the output vector 260 mayhave corresponding 2K vector slots corresponding to the 2K labels. Inthis embodiment, there are separate output nodes for combinations ofreal/fake normal and abnormal classes. For example, there may beseparate classes for real-normal, real-abnormal, fake-normal, andfake-abnormal, where in this case K is 2, i.e. normal and abnormal, butK may be any number of classes depending on the desired implementation.

Thus, the unlabeled image generated by the generator G 210 is an attemptby the generator G 210 to fool the discriminator D 250 into generatingan output indicating that the generated, or fake, image is in fact areal image. Of particular note, the generated images 240 output by thegenerator G 210 are fed into the discriminator D 250 along with reallabeled image data 220 and real unlabeled image data 230. Thediscriminator D 250 is modified, such as with regard to the lossfunction employed by the neural network of the discriminator D 250 andthe number of output nodes in the discriminator D 250 configuration, toreceive these three types of input image data and determine aclassification of the input image data into one of a plurality ofclassifications, as noted above. The loss function takes into accountnot only real labeled image data and generated (fake) image data, butalso the real unlabeled image data. Through a training process, the lossfunction of the discriminator D 250 is minimized such that the GAN 200converges and the generator G and discriminator D are trained, using themin-max gaming technique of the GAN 200, to an optimal state.

For the discriminator D 250 to be properly trained, the discriminator D250 is trained via a semi-supervised training process to learn thefeatures that are indicative of a normal medical image, an abnormalmedical image, and a generated or fake medical image. For a realunlabeled medical image input to the discriminator D 250, thediscriminator D is trained to properly identify the image as either fakeor real, i.e. being one of a real normal image or a real abnormal image,even though the discriminator D 250 may get the actual label of normalor abnormal incorrect. For a real labeled medical image input, thediscriminator D 250 is trained such that the discriminator D 250 is ableto differentiate the image as fake or being one of a real normal imageor a real abnormal image, and to determine correctly whether it is areal normal image or a real abnormal image. For generated (fake) imagesgenerated by the generator 210 as input to the discriminator D 250, thediscriminator D 250 is trained to properly identify the image as being agenerated or fake medical image.

In one illustrative embodiment of the present invention, the generator G210 is configured to receive a 100×1 input vector z 205 which isprojected and resealed. The generator G 210 then processes the inputvector z 205 via four convolutional layers with 2D-upsampling layersinterlaced in-between to scale to an appropriate image size, e.g.,128×128 image size. To avoid sparse gradients, most non-linearactivations are applied with a leaky rectified linear unit (ReLU)function which has a small negative slope for the negative domain. Thediscriminator D 250 is a similar network to the generator G 210 with aseries of convolutions with stride of 2 to replace the need formax-pooling. Dropout is used for regularization and leaky ReLU is againused except for the final one node activation with a sigmoid function. Aset of normal images may be used to trained the GAN 200 to producesamples of normal images. A second GAN may be trained using onlyabnormal training data to produce abnormal image samples. Each GAN maybe trained for many epochs, e.g., 500 epochs, where an epoch is ameasure of the number of times all of the training vectors are used onceto update the weights of the neural network nodes.

For example, the example embodiment shown in FIGS. 2A and 2B may beprimarily descriptive of a K+1 class implementation where only onegenerator is utilized. In other illustrative embodiments, there may bemultiple different generators, resulting in multiples of K classes. Forexample, in the 2K class embodiment mentioned above, there may be 2GANs, or generators of a GAN, for use in a binary classification, e.g.,actual versus fake medical image. In the 2K classificationimplementation, one generator may generate fake medical images thatinclude abnormalities and the other generator may generate fake medicalimages that do not include abnormalities. Thus, the generators are eachtrained to generate fake images that approximate real images, withregard to abnormal real images or normal real images, respectively.

It should be appreciated that the selection of the number ofconvolutional layers of the neural networks, image size, use of leakyReLU, and other configuration elements discussed above may vary based onthe desired implementation of the present invention and the illustrativeembodiments of the present invention are not limited to thoseconfiguration elements mentioned above. This is just one illustrativeembodiment provided for illustrative purposes.

The GAN architecture 200 shown in FIGS. 2A and 2B may be trained using asemi-supervised training technique. The main difference between asemi-supervised GAN implementation and an unsupervised GAN is thestructure of the loss function of the neural network of thediscriminator D 250 to incorporate both labeled and unlabeled real imagedata. The loss function can be divided into three parts. The outputlayer of the discriminator D 250, in one illustrative embodiment, hasK+1 classes, where K=2 for normal and abnormal, and the K+1 class is forgenerated (fake) images. The loss function L is defined for each type ofdata (Llabeled, Lunlabeled, Lgenerated) separately and the total loss isused in optimization of the GAN 200:L=Ll _(abeled) +L _(unlabeled) +L _(generated)  (3)L _(labeled) =−E _(xy-pdata) log p _(model)(y|X,y<K+1)  (4)L _(unlabeled) =−E _(x-pdata) log(1−p _(model)(y=K+1|x)  (5)L _(generated) =−E _(x-G) log p _(model)(y=K+1|x)  (6)where x corresponds to an image, y corresponds to the label, pdata isthe real data distribution, G is the generator, and pmodel(.|.) is thepredicted class probability. Thus, the loss function L takes intoaccount an error for each type of image fed-forward through thediscriminator D 250, and is used to update the weights of the nodes ofthe discriminator D 250 through a stochastic gradient descent basedtraining, or other training methodology implemented by training logic270.

As the loss function for unlabeled data shows, these samples can beclassified as any of the K classes of interest (K=2 here) and contributeto loss when they are classified as generated class K+1. Similar lossfunctions may be provided for implementations in which there are 2Kclasses with separate error components for the various classes in suchan implementation. As a result, this GAN architecture 200 allows theunlabeled real data to contribute to learning, reducing the amount oflabeling effort required to achieve a desired level of accuracy.

For one illustrative implementation of the GAN 200 in FIGS. 2A and 2B isconfigured and implemented to identify cardiac abnormalities in chestX-rays. It should be appreciated, however, that the GAN 200 may beutilized with any image data sets, whether medical or otherwise.Moreover, with regard to medical images, the GAN 200 may be utilizedwith any anatomical portions of a biological entity, and any imagingtechnologies or modalities, without departing from the spirit and scopeof the present invention.

To demonstrate the improvements made by the illustrative embodiment, inone illustrative embodiment in which the GAN 200 is employed to identifycardiac abnormalities in chest X-ray images, an example implementationwas generated by the present inventors, two datasets of chest X-rayimages were used. For example, one dataset was the National Institute ofHealth (NIH) prostate, lung, colorectal, and ovarian (PLCO) cancerdataset, while the other dataset was from the NIH Chest X-ray collectionfrom Indiana University. In the NIH PLCO dataset (Dataset 1) there wereapproximately 196,000 X-ray digital images of which a subset ofapproximately 36,000 frontal chest X-rays were chosen. A subset of 4500images were used with labels of normal or abnormal which weresubsequently rescaled to 128×128 pixels and histogram equalized. FIG. 3is an example diagram illustrating sample chest X-ray images from theNIH PLCO dataset with the left column corresponding to normal chestX-rays and the right column corresponding to abnormal chest X-rays dueto cardiovascular abnormality. The abnormal class samples wereoriginally tagged for any patient with cardiomegaly, congestive heartfailure, or cardiac abnormality. A subsample of 100 images were taken toconfirm correct ground-truthing with a trained radiologist. The seconddataset (Dataset 2) from Indiana University, was used to examineperformance of deep learned classifiers on different data sourcedomains. 400 cases of normal chest X-rays were used and 313 cases ofcardiomegaly (as the abnormal class) were used. The datasets were splitinto three groups, i.e. training, validation, and testing, in an80:10:10 ratio.

The GAN 200 was trained on Dataset 1 and then tested using Dataset 2.Thereafter, the GAN 200 was trained on both datasets by treating Dataset1 as labeled image data 220 and Dataset 2 as unlabeled image data 230.After training, the GAN 200 was tested on Dataset 2.

The results of the above implementation of the GAN 200 and the trainingand testing of the GAN 200, demonstrate the ability of the illustrativeembodiments of the present invention to produce generated medical imagesthat resemble chest X-rays from a qualitative perspective. Afterwards,vectors from the normal distribution (noise input to the generator) wererandomly sampled to be fed forward through the generator G 210 network.As shown in FIG. 4 , the sampled generated (fake) images capture theglobal structural elements such as the lungs, spine, heart, and visualsignatures such as the ribs, aortic arch, and the unique curvature ofthe lower lungs.

The results of the above implementation of the GAN 200 furtherdemonstrate improved performance for image classification tasks whenlabeled data is scarce. As shown in FIGS. 5A and 5B, it can be seen thatthe GAN 200 requires an order of magnitude fewer labeled samples toachieve comparable results to a convolutional neural network (CNN)trained on Dataset 1. For example, the semi-supervised GAN 200 model ofthe illustrative embodiments needed only 10 labeled medical images foreach class to achieve an accuracy of 73.08% while the conventional CNNrequired somewhere between 250 to 500 labeled medical images to achievea similar accuracy.

The results of the above implementation of the GAN 200 furtherillustrate an improved performance of the classifier, e.g.,discriminator D 250. Assuming each dataset has its associated biasesfrom data collection, usually there is a drop in performance when theGAN is tested on a new dataset. When trained on 80% of Dataset 1, aconventional CNN is able to achieve 81.93% accuracy on a held-out 10%test set from Dataset 1. However, when the same conventional CNN istested on all of Dataset 2, the accuracy drops to 57.8%, which is ahallmark over overfitting, as shown in the table of FIG. 6 .

The semi-supervised GAN of the illustrative embodiments, under the sametraining scenario is more robust as it only drops to 76.4% in accuracyon Dataset 2, as shown in FIG. 6 . Furthermore, when the semi-supervisedGAN of the illustrative embodiments is trained on 100% of Dataset 1 withlabels and 80% of Dataset 2 without labels, an accuracy of 93.7% wasable to be achieved when tested on a 20% held-out from Dataset 2. Thus,without any labeling of Dataset 2, the classifier (e.g., discriminator D250) of the GAN 200 is able to achieve high accuracy. Thus, theillustrative embodiments provide a low cost solution for modeladaptation for a new data source as the GAN mechanisms of theillustrative embodiments remove the need for costly labeling of the datafrom the new data source prior to re-training.

Thus, the deep generative adversarial network (GAN) of the illustrativeembodiments is able to learn the visual structure in medical imagingdomains, such as in the chest X-ray medical imaging domain and others.Generated samples from the generator G network of the GAN present boththe global and local structure that define particular classes of medicalimages. The semi-supervised GAN architecture of the illustrativeembodiments is capable of learning from both labeled and unlabeledmedical image data. As a result, the annotation effort is reducedconsiderably while being able to achieve similar performance throughsupervised training techniques. This may be attributed to the ability ofthe GAN architecture of the illustrative embodiments being able to learnstructure in the unlabeled medical imaging data in a supervised learningfashion which significantly offsets the low number of labeled medicalimage data samples. In addition, the semi-supervised GAN architecture ofthe illustrative embodiments is robust to data source domain issues asdemonstrated by the relatively smaller drop in accuracy of thesemi-supervised GAN architecture relative to conventional supervised CNNapproaches. Thus, if re-training the semi-supervised GAN architecture ina new domain is feasible, one can use unlabeled medical imaging datafrom the new domain rather than having to endure the costly process oflabeling medical imaging data as would be needed in the conventionalsupervised CNN approaches.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1-3 are provided hereafter as exampleenvironments in which aspects of the illustrative embodiments may beimplemented. It should be appreciated that FIGS. 1-3 are only examplesand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the presentinvention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIGS. 7-8 are directed to describing an example cognitive system forhealthcare applications which implements a medical image viewerapplication 730 for viewing medical images and obtaining informationabout the medical images of particular patients. The cognitive systemmay also provide other cognitive functionality including treatmentrecommendations, patient electronic medical record (EMR) analysis andcorrelation with medical imaging data, and various other types ofdecision support functionality involving cognitive analysis andapplication of computer based artificial intelligence or cognitive logicto large volumes of data regarding patients. In some illustrativeembodiments, the cognitive system may implement a request processingpipeline, such as a Question Answering (QA) pipeline (also referred toas a Question/Answer pipeline or Question and Answer pipeline) forexample, request processing methodology, and request processing computerprogram product with which the mechanisms of the illustrativeembodiments are implemented. These requests may be provided as structureor unstructured request messages, natural language questions, or anyother suitable format for requesting an operation to be performed by thehealthcare cognitive system.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, medical image analysis logic, and the like, for example, aswell as machine learning logic, which may be provided as specializedhardware, software executed on hardware, or any combination ofspecialized hardware and software executed on hardware. The logic of thecognitive system implements the cognitive operation(s), examples ofwhich include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches, for example, medical diagnostic and treatment recommendations,medical image analysis logic, and other types of logic that emulatehuman thought processes using specially configured computing mechanisms.IBM Watson™ is an example of one such cognitive system with which themechanisms of the illustrative embodiments may be utilized or in whichthe mechanisms of the illustrative embodiments may be implemented.

FIG. 7 depicts a schematic diagram of one illustrative embodiment of acognitive system 700 implementing a medical image viewer application 730in a computer network 702. The cognitive system 700 is implemented onone or more computing devices 704A-D (comprising one or more processorsand one or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 702. For purposes of illustration only, FIG. 7 depicts thecognitive system 700 being implemented on computing device 704A only,but as noted above the cognitive system 700 may be distributed acrossmultiple computing devices, such as a plurality of computing devices704A-D. The network 702 includes multiple computing devices 704A-D,which may operate as server computing devices, and 710-712 which mayoperate as client computing devices, in communication with each otherand with other devices or components via one or more wired and/orwireless data communication links, where each communication linkcomprises one or more of wires, routers, switches, transmitters,receivers, or the like.

In some illustrative embodiments, the cognitive system 700 and network702 enables question processing and answer generation (QA) functionalityfor one or more cognitive system users via their respective computingdevices 710-712. In other embodiments, the cognitive system 700 andnetwork 702 may provide other types of cognitive operations including,but not limited to, request processing and cognitive response generationwhich may take many different forms depending upon the desiredimplementation, e.g., cognitive information retrieval,training/instruction of users, cognitive evaluation of data, such asmedical imaging data, or the like. Other embodiments of the cognitivesystem 700 may be used with components, systems, sub-systems, and/ordevices other than those that are depicted herein.

In some illustrative embodiments, the client computing devices 710 and712 may be used as a mechanism for logging onto or otherwise accessingthe cognitive system 700 for purposes of viewing medical imaging studiesfor patients and perform operations for classifying and/or corroboratingautomated classification of such medical imaging studies. For example, aradiologist or other medical imaging subject matter expert (SME) mayutilize a client computing device 710 to access the services andfunctionality provided by the cognitive system 700 and the medical imageviewer application 730 to view medical images of one or more medicalimaging studies stored in the corpus 740 for one or more patients. Theuser of the client computing device 710 may view the medical images andperform operations for annotating the medical images, adding notes topatient electronic medical records (EMRs), corroborate automaticallyidentified classifications of the medical images and/or overrideincorrect classifications, and any of a plethora of other operationsthat may be performed through human-computer interaction based on thehuman's viewing of the medical images via the cognitive system 700.

As noted above, in some illustrative embodiments, the cognitive system700 may be configured to implement a request processing pipeline thatreceive inputs from various sources. The requests may be posed in theform of a natural language question, natural language request forinformation, natural language request for the performance of a cognitiveoperation, or the like. For example, the cognitive system 700 receivesinput from the network 702, a corpus or corpora of electronic documents706, cognitive system users, and/or other data and other possiblesources of input. In one embodiment, some or all of the inputs to thecognitive system 700 are routed through the network 702. The variouscomputing devices 704A-D on the network 702 include access points forcontent creators and cognitive system users. Some of the computingdevices 704A-D include devices for a database storing the corpus orcorpora of data 706 (which is shown as a separate entity in FIG. 1 forillustrative purposes only). Portions of the corpus or corpora of data706 may also be provided on one or more other network attached storagedevices, in one or more databases, or other computing devices notexplicitly shown in FIG. 7 . The network 702 includes local networkconnections and remote connections in various embodiments, such that thecognitive system 700 may operate in environments of any size, includinglocal and global, e.g., the Internet.

The request processing pipeline of the cognitive system 700 maycomprises a plurality of stages for processing an input question/requestbased on information obtained from the corpus or corpora of data 706and/or 740. The pipeline generates answers/responses for the inputquestion or request based on the processing of the inputquestion/request and the corpus or corpora of data 706, 740. In someillustrative embodiments, the cognitive system 700 may be the IBMWatson™ cognitive system available from International Business MachinesCorporation of Armonk, N.Y., which is augmented with the mechanisms ofthe illustrative embodiments described herein. More information aboutthe pipeline of the IBM Watson™ cognitive system may be obtained, forexample, from the IBM Corporation website, IBM Redbooks, as well as inYuan et al., “Watson and Healthcare,” IBM developerWorks, 2011 and “TheEra of Cognitive Systems: An Inside Look at IBM Watson and How it Works”by Rob High, IBM Redbooks, 2012.

As shown in FIG. 7 , the cognitive system 700 may operate in conjunctionwith a semi-supervised GAN classification system 720, in accordance withthe mechanisms of the illustrative embodiments. The semi-supervised GANclassification system 720 may be implemented in specialized hardware,software executed on hardware, or any combination of specializedhardware and software executed on hardware. The semi-supervised GANclassification system 720 comprises one or more medical image GANs 726specifically configured and trained in the manner previous describedabove with regard to one or more of the illustrative embodiments.

That is, the semi-supervised GAN classification system 720 comprises themedical image GAN(s) 726 which are trained using the labeled trainingimage data 722 and unlabeled training image data 724 via the trainingengine logic 728. The labeled training image data 722, as discussedabove, comprises a relatively small set of medical image data that islabeled by subject matter experts (SMEs), whereas the unlabeled trainingimage data 724 is relatively larger and does not have such labels. Boththe labeled and unlabeled training data 722, 724, as well as generated(fake) images generated by a generator G of the GAN 726 are used totrain the GAN to produce additional generated (fake) medical images 725approximating real medical images which can be used to augment theunlabeled training image data set 724, e.g., fake images 725 similar tothat shown in FIG. 4 . In addition, the discriminator D of the GAN 726is trained to differentiate input medical image data into K+1 or 2Kclasses of medical images, depending on the implementation, usinglabeled and unlabeled training data as discussed above. Thediscriminator D of the medical imaging GAN(s) 726 may then be used as aclassifier to classify medical image data from various medical imagingstudies of patients as may be provided in the corpus 740, for example,and provide the classification information to the cognitive system 700for performance of cognitive operations and/or viewing by a user via themedical image viewer application 730. The training engine 728 comprisesthe logic for determining how to modify weights of nodes in the neuralnetworks of the generator G and discriminator D so as to converge theGANs 726 to an optimal state.

In one illustrative embodiment, the discriminator D of the GAN 726, oncetrained may be utilized as a classifier for classifying medical imagesinto one of a plurality of classes. The classes comprise a class fornormal medical images, i.e. medical images where no abnormality isidentified, and one or more other classes indicative of one or moreabnormalities or diseases. The number of abnormalities for which a GAN726 is trained is implementation dependent. In some implementationsseparate GANs 726 may be trained and utilized for different types ofabnormalities or diseases. Thus, a medical image, i.e. the datarepresenting the medical image, may be submitted to a plurality ofdifferent GANs 726 which have been separately trained using themechanisms of the illustrative embodiments, for different abnormality ordisease classification and may operate on the medical image data inparallel to determine classifications of the medical image with regardto the different abnormalities or diseases. It can be appreciated thatfor one GAN 726, the outcome may be that the medical image is a normalmedical image (because that GAN is not trained to identify anabnormality of the type actually present in the medical image data),whereas for a different GAN 726 the output may indicate an abnormality(because that GAN is trained to identify the abnormality of the typepresent in the medical image data). Thus, an aggregation of the outputsof the GANs 726 may be generated by the semi-supervised GANclassification system 720 and provided to the cognitive system 700 foruse in cognitive operations and/or viewing by a user via the medicalimage viewer application 730.

As previously mentioned above, one benefit of the GAN based architectureof the illustrative embodiments is the robustness of the architecturewith regard to handling new medical image data sources. Thus, forexample, if a new client of the cognitive system 700 service, such as anew radiology lab, a new CT scan facility, a new hospital, or the like,is registered with the cognitive system 700, rather than having to havethe client label the medical image data that it wishes to utilize withthe cognitive system 700 and the semi-supervised GAN classificationsystem 720 for purposes of classifying the medical image data, themechanisms of the illustrative embodiments allow the trained GAN 726 tobe adapted or re-trained for the new data source, or a new instance ofthe trained GAN 726 may be adapted or re-trained for the new datasource, without having to label the new data source's medical imagedata.

For example, assume that a server 704C is associated with a medicalimaging laboratory that decides to utilize the services of the cognitivesystem 700 and medical image viewer application 730. The server 704Cprovides a new data source 750 having new unlabeled medical image datathat has not been previously processed by the cognitive system 700 andsemi-supervised GAN classification system 720. The semi-supervised GANclassification system 720 may adapt or re-train the GAN 726 foroperation with the new data source 750. However, in some situations, itmay not be possible to re-train the GAN 726 when presented with a newdata source at a new client site, e.g., server 704C. In such asituation, if the GAN 726 has been trained, at the time of training withthe new client's own data, there may be a smaller reduction in accuracywhen the new client's data is utilized.

Even in the case where re-training of the GAN 726 for use with a newclient, e.g., server 704C, is an option, typically one does not have theluxury of obtaining all of the new client data in a labelled format, andthe client system 704C may only be able to provide their data asunlabeled data. In this scenario, methodologies may be utilized in whichthe new client's unlabeled data is used to train the GAN 726 using thesemi-supervised architecture, which is specifically configured to permitthe use of unlabelled data. These methodologies may, in cases where somelabeled data is able to be provided, such as from a third party sourceor from a labeling of a small subset of the new client's data, mayinvolve some re-training of the GAN 726 based on the small set oflabeled data, but with a relatively larger set of unlabeled data.

For example, in some illustrative embodiments, a first methodology maycomprise a semi-supervised GAN training architecture as describe abovein which only a relatively small training dataset is utilized thatincludes only labeled medical image data. In one illustrativeembodiment, a small number of the medical images present in theunlabeled medical image data 750 of the new client need to be labeledand provided as training input into the semi-supervised GANclassification system 720 to re-train the medical image GAN 726. Asshown in FIG. 5B above, a small labeled training medical image data setmay be used to obtain a relatively high level of accuracy in thetraining of the medical image GAN 726. As shown in FIG. 6 , while asmall reduction in accuracy may be obtained from training only on asmall labeled training medical image data set, the accuracy is stillcomparable to the accuracy of conventional supervised CNNs trained onrelatively large labeled medical image datasets.

In one illustrative embodiment, a second methodology may comprise asemi-supervised GAN 726 that is re-trained using both a small set oflabeled medical image data from a known and trusted source of labeledmedical image data, such as NIH, for example, and a relatively largerset of unlabeled medical image data. In this methodology, the new andunlabeled medical image data, or a portion thereof, from the new datasource 750 may be utilized as the set of unlabeled medical image datafor re-training the semi-supervised GAN 726. In either the first orsecond methodology, extensive labeling of the unlabeled medical imagedata in the new data source 750 is not required in order for thesemi-supervised GAN classification system 720 to be trained for use withthe new data source 750 due to the architecture provided by themechanisms of the illustrative embodiments which permit training of theGAN 726 based on both labeled and unlabeled medical image data.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 8 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 8 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 800 is an example of a computer, such as a server 704A-D orclient 710-712 in FIG. 7 , in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention are located. In one illustrative embodiment, FIG. 8 representsa server computing device, such as a server 704A, which, whichimplements a cognitive system 700 and medical image viewer application730, where the server 704A further is specifically configured andexecutes hardware and/or software logic to implement the semi-supervisedGAN classification system 720 of FIG. 7 .

In the depicted example, data processing system 800 employs a hubarchitecture including North Bridge and Memory Controller Hub (NB/MCH)802 and South Bridge and Input/Output (I/O) Controller Hub (SB/ICH) 804.Processing unit 806, main memory 808, and graphics processor 810 areconnected to NB/MCH 802. Graphics processor 810 is connected to NB/MCH802 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 812 connectsto SB/ICH 804. Audio adapter 816, keyboard and mouse adapter 820, modem822, read only memory (ROM) 824, hard disk drive (HDD) 826, CD-ROM drive830, universal serial bus (USB) ports and other communication ports 832,and PCI/PCIe devices 834 connect to SB/ICH 804 through bus 838 and bus840. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 824 may be, for example, a flashbasic input/output system (BIOS).

HDD 826 and CD-ROM drive 830 connect to SB/ICH 804 through bus 840. HDD826 and CD-ROM drive 830 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 836 is connected to SB/ICH 804.

An operating system runs on processing unit 806. The operating systemcoordinates and provides control of various components within the dataprocessing system 800 in FIG. 8 . As a client, the operating system is acommercially available operating system such as Microsoft® Windows 10®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 800.

As a server, data processing system 800 may be, for example, an IBM®eServer™ System p° computer system, running the Advanced InteractiveExecutive) (AIX® operating system or the LINUX® operating system. Dataprocessing system 800 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 806.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 826, and are loaded into main memory 808 for execution byprocessing unit 806. The processes for illustrative embodiments of thepresent invention are performed by processing unit 806 using computerusable program code, which is located in a memory such as, for example,main memory 808, ROM 824, or in one or more peripheral devices 826 and830, for example.

A bus system, such as bus 838 or bus 840 as shown in FIG. 8 , iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 822 or network adapter 812 of FIG. 8 , includes one or moredevices used to transmit and receive data. A memory may be, for example,main memory 808, ROM 824, or a cache such as found in NB/MCH 802 in FIG.8 .

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 7 and 8 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 7and 8 . Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 800 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 800 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 800 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 9 is a flowchart outlining an example operation for training asemi-supervised GAN in accordance with one illustrative embodiment. Asshown in FIG. 9 , the operation starts by configuring the GAN to operateon labeled/unlabeled medical image data input using K+1 or 2Kclassifications (step 910). The GAN is further configured with a lossfunction of the discriminator to include error components for each ofthe labeled, unlabeled, and fake images (step 920). The generator of theGAN then generates a fake image based on an input noise vector z (step930). In addition, real normal image data and real abnormal image dataare provided to the discriminator of the GAN along with the generated(fake) image data (step 940). The discriminator provides aclassification of the image data and a calculated loss based on theconfigured loss function (step 950). The operation of the GAN, e.g., theweights of nodes in the generator and discriminator, is then modifiedbased on the loss function calculation and training logic that operatesto minimize the loss function (step 960). A determination is made as towhether or not the GAN training has converged (step 970). If so, theoperation terminates. Otherwise, the operation returns to step 930 withfurther training based on additional fake image generation.

FIG. 10 is a flowchart outlining an operation for re-training asemi-supervised GAN for a new data source in accordance with oneillustrative embodiment. As shown in FIG. 10 , the operation comprisesreceiving the registration of a new data source (step 1010). Amethodology for re-training the semi-supervised medical image GAN isselected for the new data source (step 1020). The selected methodologyis then applied without requiring extensive labeling of the new datasource data (step 1030). After training, the medical image data in thenew data source is then classified using the re-trained GAN (step 1040)and the operation terminates.

It should be appreciated that while the above described illustrativeembodiments are especially well suited for implementation with medicalimage data and classifying medical images, the illustrative embodimentsare not limited to such. Rather, the mechanisms of the illustrativeembodiments may be utilized with any implementation in which image data,whether medical or otherwise, is classified into one of a plurality ofclasses. The principles of the illustrative embodiments with regard toproviding an architecture that can be trained using both labeled andunlabeled image data are equally applicable regardless of the particulartype of image data being operated on. Moreover, the various cognitiveoperations that are supported by the classifications may vary dependingon the type of image data operated on and the classifications employed.For example, facial recognition mechanisms, biometric securitymechanisms, and the like, may all implement classifications of imagedata and may benefit from the implementation of a GAN based architecturesuch as that described herein. Thus, many modifications may be made tothe mechanisms described above without departing from the spirit andscope of the present invention.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisinga processor and a memory, the memory comprising instructions that areexecuted by the processor to configure the processor to implement amachine learning training model, the method comprising: training, by themachine learning training model, a machine learning model based on a setof training medical images to identify anomalies in medical images; andapplying the trained machine learning model to new medical image inputsto classify the medical images as having an anomaly or not, whereintraining the machine learning model comprises training a discriminatorof a generative adversarial network (GAN) based on training data, inputto the discriminator comprising actual labeled medical image data,actual unlabeled medical image data, and generated medical image datagenerated by an image generator of the GAN.
 2. The method of claim 1,further comprising training an image generator of the GAN to generatemedical images approximating actual medical images, at least by:receiving, by the image generator of the GAN, at least one medical imageshowing an anatomical structure; inputting, by a noise generator, anoise input to the image generator; generating, by the image generator,at least one generated image based on a combination of the at least onemedical image and the noise input; providing the at least one medicalimage and the at least one generated image to the discriminator of themachine learning training image; analyzing, by the discriminator, the atleast one medical image and the at least one generated image to labelthe at least one medical image and the at least one generated image aseither being an actual medical image or a generated medical image; andmodifying an operational parameter of the generator based on the resultsof analyzing the at least one medical image and the at least onegenerated image by the discriminator.
 3. The method of claim 1, whereinthe GAN is trained to classify medical images with regard to whether ornot the medical images contain an abnormality.
 4. The method of claim 1,wherein the discriminator is trained, using an adversarial trainingtechnique based on the set of training medical images, to discriminatebetween medical images showing anomalies and medical images showingnormal medical conditions.
 5. The method of claim 1, wherein trainingthe image generator comprises training the image generator based on bothlabeled training medical images and non-labeled medical images, andwherein the training is based on a feedback obtained from an output ofthe discriminator.
 6. The method of claim 1, wherein the generatedmedical image data is generated by the image generator of the GAN basedon input noise, and wherein training, by the machine learning trainingmodel, the image generator to generate medical images approximatingactual medical images comprises: training the discriminator of the GANto separately classify generated medical images from actual medicalimages and separately classifying actual medical images showing a normalmedical condition from actual medical images showing an abnormal medicalcondition; and training the image generator to generate medical imagesthat cause the discriminator to fail to correctly separately classifythe generated medical images from actual medical images for apredetermined ratio of instances indicating convergence of the trainingof the GAN.
 7. The method of claim 1, wherein the discriminator of theGAN is a convolutional neural network with K+1 output nodes, where K isa number of classes of abnormalities that the machine learning model isconfigured to identify in input medical images and the K+1 class is aclass indicating an input medical image to be a generated medical imagefrom the image generator, and wherein the discriminator outputs, foreach input medical image, a corresponding vector output having K+1values indicating into which class the discriminator classifies theinput medical image.
 8. The method of claim 1, wherein the discriminatoris a convolutional neural network with 2K output nodes, where K is anumber of classes of abnormalities that the machine learning model isconfigured to identify in input medical images and for each class in theK number of classes, there is a separate output node indicating whetherthe input medical image is a generated medical image from the imagegenerator, or an actual medical image, and wherein the discriminatoroutputs, for each input medical image, a corresponding vector outputhaving 2K values indicating into which class the discriminatorclassifies the input medical image.
 9. The method of claim 1, furthercomprising: configuring a loss function of a discriminator of the GAN toinclude error components for each of actual labeled medical images,actual unlabeled medical images, and generated medical images, whereintraining the image generator of the GAN to generate medical imagesapproximating actual medical images comprises minimizing the lossfunction of the discriminator.
 10. The method of claim 1, wherein thegenerator is one of a plurality of generators, and wherein at least onegenerator in the plurality of generators is trained to generate medicalimages approximating actual medical images having a normal medicalcondition depicted, and wherein at least one other generator in theplurality of generators is trained to generate medical imagesapproximating actual medical images having an abnormal medical conditiondepicted.
 11. The method of claim 1, wherein the discriminator generatesa classification output that classifies input image data into one of aplurality of classifications, the plurality of classificationscomprising a first classification for real-normal image data indicatingthe input image data to be actual image data representing a normalmedical condition, at least one second classification for real-abnormalimage data indicating the input image data to be actual image datarepresenting a corresponding abnormal medical condition, and a thirdclassification for generated image data indicating the input image datato be image data generated by the image generator.
 12. A computerprogram product comprising a computer readable storage medium having acomputer readable program stored therein, wherein the computer readableprogram, when executed on a data processing system, causes the dataprocessing system to implement a machine learning training model thatoperates to: train a machine learning model based on a set of trainingmedical images to identify anomalies in medical images; and apply thetrained machine learning model to new medical image inputs to classifythe medical images as having an anomaly or not, wherein training themachine learning model comprises training a discriminator of agenerative adversarial network (GAN) based on training data, input tothe discriminator comprising actual labeled medical image data, actualunlabeled medical image data, and generated medical image data generatedby an image generator of the GAN.
 13. The computer program product ofclaim 12, wherein the computer readable program further causes themachine learning training model to train by the machine learningtraining model, an image generator of the GAN to generate medical imagesapproximating actual medical images, at least by: receiving, by theimage generator of the GAN, at least one medical image showing ananatomical structure; inputting, by a noise generator, a noise input tothe image generator; generating, by the image generator, at least onegenerated image based on a combination of the at least one medical imageand the noise input; providing the at least one medical image and the atleast one generated image to the discriminator of the machine learningtraining image; analyzing, by the discriminator, the at least onemedical image and the at least one generated image to label the at leastone medical image and the at least one generated image as either beingan actual medical image or a generated medical image; and modifying anoperational parameter of the generator based on the results of analyzingthe at least one medical image and the at least one generated image bythe discriminator.
 14. The computer program product of claim 12, whereinthe is a discriminator is trained, using an adversarial trainingtechnique based on the set of training medical images, to discriminatebetween medical images showing anomalies and medical images showingnormal medical conditions.
 15. The computer program product of claim 12,wherein the computer readable program further causes the machinelearning training model to train the image generator at least bytraining the image generator based on both labeled training medicalimages and non-labeled medical images, and wherein the training is basedon a feedback obtained from an output of the discriminator.
 16. Thecomputer program product of claim 12, wherein the generated medicalimage data is generated by the image generator of the GAN based on inputnoise, and wherein the computer readable program further causes themachine learning training model to train the image generator to generatemedical images approximating actual medical images at least by: trainingthe discriminator of the GAN to separately classify generated medicalimages from actual medical images and separately classifying actualmedical images showing a normal medical condition from actual medicalimages showing an abnormal medical condition; and training the imagegenerator to generate medical images that cause the discriminator tofail to correctly separately classify the generated medical images fromactual medical images for a predetermined ratio of instances indicatingconvergence of the training of the GAN.
 17. The computer program productof claim 12, wherein the discriminator of the GAN is a convolutionalneural network with K+1 output nodes, where K is a number of classes ofabnormalities that the machine learning model is configured to identifyin input medical images and the K+1 class is a class indicating an inputmedical image to be a generated medical image from the image generator,and wherein the discriminator outputs, for each input medical image, acorresponding vector output having K+1 values indicating into whichclass the discriminator classifies the input medical image.
 18. Thecomputer program product of claim 12, wherein the discriminator is aconvolutional neural network with 2K output nodes, where K is a numberof classes of abnormalities that the machine learning model isconfigured to identify in input medical images and for each class in theK number of classes, there is a separate output node indicating whetherthe input medical image is a generated medical image from the imagegenerator, or an actual medical image, and wherein the machine learningmodel discriminator outputs, for each input medical image, acorresponding vector output having 2K values indicating into which classthe machine learning model discriminator classifies the input medicalimage.
 19. The computer program product of claim 12, wherein thecomputer readable program further causes the machine learning trainingmodel to: configure a loss function of a discriminator of the GAN toinclude error components for each of actual labeled medical images,actual unlabeled medical images, and generated medical images, whereintraining the image generator of the GAN to generate medical imagesapproximating actual medical images comprises minimizing the lossfunction of the discriminator.
 20. An apparatus comprising: at least oneprocessor; and at least one memory coupled to the at least oneprocessor, wherein the at least one memory comprises instructions which,when executed by the at least one processor, cause the at least oneprocessor to implement a machine learning training model that operatesto: train a machine learning model based on a set of training medicalimages to identify anomalies in medical images; and apply the trainedmachine learning model to new medical image inputs to classify themedical images as having an anomaly or not, wherein training the machinelearning model comprises training a discriminator of the GAN based ontraining data, input to the discriminator, comprising actual labeledmedical image data, actual unlabeled medical image data, and generatedmedical image data generated by the image generator of the GAN.